In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effectiveness against previous constrained and non-constrained algorithms for the given task. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Ares, M. E., Parapar, J., & Barreiro, Á. (2009). Avoiding bias in text clustering using constrained K-means and May-not-links. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5766 LNCS, pp. 322–329). https://doi.org/10.1007/978-3-642-04417-5_32
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